[Columbia University in the City of New York] Columbia+ online course: Quantitative Techniques (US$149, self-paced) – $0.00 (w/ PIDAY2025 promo code; ends April 30)

I’m a few days late in noticing/reporting this, as I came across a couple of social media posts that mention this promotion, which celebrates Pi Day 2025 and the offer ends April 30th, 2025 (X/Twitter, LinkedIn). Can confirm that the PIDAY2025 promo code (at Checkout) is working as per attached screenshots.

NOTE: to enroll into, and complete, Columbia+ online course ‘Quantitative Techniques’ by Columbia University’s Professor Andrew Gelman, one must use an existing Columbia+ user account (its free to create a new account via e-mail signup).

Quantitative Techniques

Learn statistical concepts and their application, including data analysis, probability trees, decision making, and making valid inferences.

Quantitative Techniques
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ENROLL

• Modules: 12
• Weekly Effort: 5 hours
• Discipline: Social Sciences, Natural, AI & Data Science
• School: International and Public Affairs
• Format: Online
• Cost: $149.00

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Course Description

• Understand statistical concepts and apply them through valid inference-making, data exploration, and measurement analysis.
• Learn about probability trees, the law of large numbers, and decision-making processes.
• Master the logic of hypothesis testing, constructing confidence intervals, and accounting for uncertainty in statistical inferences.
• Develop analytical and critical thinking skills to conduct successful data analyses and make informed decisions in real-world scenarios.

What You Will Learn

By the end of this course, learners will be able to:

• Develop a comprehensive understanding of statistical concepts and their practical application.
• Make valid inferences about a population based on both random and non-random samples.
• Explore data effectively to gain insights about the world and draw meaningful conclusions.
• Master measurement techniques and linear regression analysis for data interpretation and prediction.
• Acquire knowledge of probability trees, the law of large numbers, and decision making processes.
• Grasp the logic behind hypothesis testing, construct confidence intervals, and appreciate the significance of accounting for uncertainty in statistical inferences.
• Apply their knowledge to conduct robust data analyses that generate accurate and reliable insights for informed decision making.

Course Outline

• Module 1: Sampling and Adjustment
• Module 2: Learning from Data; Exploratory Data Analysis
• Module 3: Measurement
• Module 4: Introduction to Linear Regression
• Module 5: Understanding Linear Regression
• Module 6: Multiple Regression
• Module 7: Causal Identification
• Module 8: Uncertainty and the Scientific Process
• Module 9: Probability Trees
• Module 10: Law of Large Numbers
• Module 11: Decision Making
• Module 12: Putting it all Together

Statistics: Posted by da1jonty — Mar 22nd, 2025 5:21 pm

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